Image segmentation is very important for various fields. With the development of computer technology, computer technology has become more and more effective for image segmentation, and it is studied on the basis of partial differential equations. The curve representation method in plane differential geometry is expounded, with the SegNet-v2 segmentation model analyzed and tested in medical image segmentation. The test results show that the partial differential equation image segmentation algorithm can achieve more accurate segmentation, especially in medical image segmentation, which can achieve good results, and it is worth in practice to further promote.
WangY., Research of Image Segmentation Algorithm Based on Chaos Particle Swarm and Fuzzy Clustering[J], Journal of Liaoning Shihua University1(2) (2016), 15–16.
2.
KostrovB.V., BabaevS.I. and SvirinaA.G., The research of image segmentation algorithms based on Vilenkin-Crestensen functions[J], Journal of Liaoning Shihua University6(1) (2016), 3007.
3.
XieX., WangJ. and HuF., An improved floc image segmentation algorithm based on particle swarm optimization and entropic[J], Journal of Computational Information Systems15(1/2) (2017), 49.
4.
FangW., SunX., FanX., et al., Research of Hand Gesture Segmentation Algorithm Based on the Stereo Vision System[J], Journal of Sichuan University of Science & Engineering4(2) (2016), 33–36.
5.
RenY., An image segmentation algorithm based on K-means algorithm and otsu[J], Boletin Tecnico/technical Bulletin55(9) (2017), 78–83.
6.
TangF.X. and YangY.F., Research of color image segmentation algorithm based on asymmetric kernel density estimation[J], Journal of Computational Methods in Sciences & Engineering17(3) (2017), 1–8.
7.
HuangH., MinghuiW.U., WangX., et al., Image Recognition of Tender Leaves Based on Improved Watershed Algorithm in Tea[J], Guizhou Agricultural Sciences4(6) (2018), 15–16.
8.
HouS., WangY., TangQ., et al., Crimp Terminals Sectional Contour Extraction Based on Partial Differential Equation and Canny Operator[J], Journal of Shanxi University8(1) (2017), 66–69.
9.
SongH. and WangY., A Spectral-Spatial Classification of Hyperspectral Images Based on the Algebraic Multigrid Method and Hierarchical Segmentation Algorithm[J], Remote Sensing8(4) (2016), 296.
10.
MiaoJ., HuangT.Z., ZhouX., et al., Image Segmentation Based On an Active Contour Model of Partial Image Restoration with Local Cosine Fitting Energy[J], Information Sciences8(5) (2018), 447.
11.
ShiZ., WeihangC. and LuoZ., Image segmentation algorithm based on hyperpixel and Bayesian combination [J], Computer Engineering and Applications54(21) (2018), 188–192+221.
12.
YuzhuC. and YunfeiC., Wood defect image segmentation algorithm based on fractional order CV model [J], Forestry Machinery and Woodworking Equipment046(004) (2018), 44–47, 51.
13.
ZhanW., AnD.P., YangL., et al., Image segmentation algorithm of temperature indicating paint based on FCM clustering [J], Journal of Aeronautical Dynamics33(003) (2018), 604–610.
14.
XuegangH. and SiqiY., Image segmentation algorithm based on FCM clustering [J], Computer Engineering and Design39(001) (2018), 159–164.
15.
JunZ., ZhihengZ. and XinshanZ., Image segmentation algorithm based on 2D histogram in polar coordinates [J], Journal of Tianjin University (Natural Science and Engineering Technology Edition) v.51; 327(06) (2018), 108–115.
16.
JunhuiW., LiejunW. and JiweiQ., Two dimensional Otsu image segmentation algorithm based on improved FA optimization [J], Journal of Xinjiang University (NATURAL SCIENCE EDITION)035(001) (2018), 60–65.
17.
ZhuoW., YatingW., WenlongS., et al., Leaf image segmentation algorithm based on deep learning [J], Forest Engineering035(001) (2019), 42–46.
18.
HuanliW., KewangC., XinZ., et al., Extraction of fine leaf crop coverage based on improved k-means image segmentation algorithm [J], Acta Agricultural Machinery50(01) (2019), 49–57.
19.
YufeiS., GuifaT., YingchunY., et al., Population wheat image segmentation algorithm based on color feature rstrgmr [J], Journal of Hebei Agricultural University (2019).
20.
JiwenW., YuL., JianhuaX., et al., Application of CT image segmentation algorithm based on deep learning for accurate calculation of lesion volume of spontaneous cerebral hemorrhage [J], Chinese Journal of Radiology53(11) (2019), 941–945.
21.
GuifangZ., Research on image segmentation algorithm and its application in segmentation of pulmonary nodules [D]. (2019).
22.
JiwenW., HaoW., XiuliL., et al., Application of CT image segmentation algorithm based on deep learning for accurate calculation of lesion volume of spontaneous cerebral hemorrhage [J], Chinese Journal of Radiology053(011) (2019), 941–945.
23.
YingH., YaonanZ. and HuipingY., Right ventricular image segmentation algorithm based on OMP and K-SVD [J], Electronic Design Engineering27(06) (2019), 37–41.
24.
YangL., Research on FCM Image Segmentation Algorithm Based on spatial constraint [D]. (2019).
25.
XuanL. and XinnanSun, Image segmentation algorithm based on convolutional neural network [J], Journal of Shenyang University of Aeronautics and Astronautics2020(1), 50–57.
26.
LeiL.S. and GuanghongX., Research on underwater image segmentation algorithm based on improved adaptive gacv [J], Artificial Intelligence and Robot Research007(001) (2018), 25–33.
